Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17

Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17

Abstract This paper introduces Biblio-US17, a labeled dataset collected over 6 months from the log files of a popular public website at the University of Seville. It contains 47 million records, each including the method, uniform resource identifier (URI) and associated response code and size of eve...

Full description

Saved in:
Bibliographic Details
Main Authors: Jesús Díaz-Verdejo, Rafael Estepa, Antonio Estepa, Javier Muñoz-Calle, Germán Madinabeitia
Format: Article
Language:English
Published: SpringerOpen 2025-06-01
Series:Cybersecurity
Subjects:
Anomaly detection
Intrusion detection systems
Data acquisition
Training datasets
Web application filters
Biblio-US17 dataset
Online Access:https://doi.org/10.1186/s42400-024-00336-3
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items